Release Notes

Release 0.3.1

Major Features and Improvements

  • The latest changes made to the submodule have been pulled (#530, see also #532). This includes in particular the performance improvements to the C++ backend submitted by Julian Burella Pérez via scikit-tda/ The developer installation now includes a new dependency in robinhood hashmap. These changes do not affect functionality.

  • The example notebook classifying_shapes.ipynb has been modified and improved (#523).

  • The tutorial previously called time_series_classification.ipynb has been split into an introductory tutorial on the Takens embedding ideas (topology_time_series.ipynb) and an example notebook on gravitational wave detection (gravitational_waves_detection.ipynb) which presents a time series classification task (#529).

  • The documentation for PairwiseDistance has been improved (#525).

Bug Fixes

  • Timeout deadlines for some of the hypothesis tests have been increased to make them less flaky (#531).

Backwards-Incompatible Changes

  • Due to poor support for brew in the macOS 10.14 virtual machines by Azure, the CI for macOS systems is now run on 10.15 virtual machines and 10.14 is no longer supported by the wheels (#527)

Thanks to our Contributors

This release contains contributions from many people:

Julian Burella Pérez, Umberto Lupo, Lewis Tunstall, Wojciech Reise, and Rayna Andreeva.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.3.0

Major Features and Improvements

This is a major release which adds substantial new functionality and introduces several improvements.

Persistent homology of directed flag complexes via pyflagser

  • The pyflagser package (source, docs) is now an official dependency of giotto-tda.

  • The FlagserPersistence transformer has been added to gtda.homology (#339). It wraps pyflagser.flagser_weighted to allow for computations of persistence diagrams from directed or undirected weighted graphs. A new notebook demonstrates its use.

Edge collapsing and performance improvements for persistent homology

  • GUDHI C++ components have been updated to the state of GUDHI v3.3.0, yielding performance improvements in SparseRipsPersistence, EuclideanCechPersistence and CubicalPersistence (#468).

  • Bindings for GUDHI’s edge collapser have been created and can now be used as an optional preprocessing step via the optional keyword argument collapse_edges in VietorisRipsPersistence and in gtda.externals.ripser (#469 and #483). When collapse_edges=True, and the input data and/or number of required homology dimensions is sufficiently large, the resulting runtimes for Vietoris–Rips persistent homology are state of the art.

  • The performance of the Ripser bindings has otherwise been improved by avoiding unnecessary data copies, better managing the memory, and using more efficient matrix routines (#501 and #507).

New transformers and functionality in gtda.homology

  • The WeakAlphaPersistence transformer has been added to gtda.homology (#464). Like VietorisRipsPersistence, SparseRipsPersistence and EuclideanCechPersistence, it computes persistent homology from point clouds, but its runtime can scale much better with size in low dimensions.

  • VietorisRipsPersistence now accepts sparse input when metric="precomputed" (#424).

  • CubicalPersistence now accepts lists of 2D arrays (#503).

  • A reduced_homology parameter has been added to all persistent homology transformers. When True, one infinite bar in the H0 barcode is removed for the user automatically. Previously, it was not possible to keep these bars in the simplicial homology transformers. The default is always True, which implies a breaking change in the case of CubicalPersistence (#467).

Persistence diagrams

  • A ComplexPolynomial feature extraction transformer has been added (#479).

  • A NumberOfPoints feature extraction transformer has been added (#496).

  • An option to normalize the entropy in PersistenceEntropy according to a heuristic has been added, and a nan_fill_value parameter allows to replace any NaN produced by the entropy calculation with a fixed constant (#450).

  • The computations in HeatKernel, PersistenceImage and in the pairwise distances and amplitudes related to them has been changed to yield the continuum limit when n_bins tends to infinity; sigma is now measured in the same units as the filtration parameter and defaults to 0.1 (#454).

New curves subpackage

A new curves subpackage has been added to preprocess, and extract features from, collections of multi-channel curves such as returned by BettiCurve, PersistenceLandscape and Silhouette (#480). It contains:

  • A StandardFeatures transformer that can extract features channel-wise in a generic way.

  • A Derivative transformer that computes channel-wise derivatives of any order by discrete differences (#492).

New metaestimators subpackage

A new metaestimator subpackage has been added with a CollectionTransformer meta-estimator which converts any transformer instance into a fit-transformer acting on collections (#495).


  • A DensityFiltration for collections of binary images has been added (#473).

  • Padder and Inverter have been extended to greyscale images (#489).

Time series

  • TakensEmbedding is now a new transformer acting on collections of time series (#460).

  • The former TakensEmbedding acting on a single time series has been renamed to SingleTakensEmbedding transformer, and the internal logic employed in its fit for computing optimal hyperparameters is now available via a takens_embedding_optimal_parameters convenience function (#460).

  • The _slice_windows method of SlidingWindow has been made public and renamed into slice_windows (#460).


  • GraphGeodesicDistance has been improved as follows (#422):

    • The new parameters directed, unweighted and method have been added.

    • The rules on the role of zero entries, infinity entries, and non-stored values have been made clearer.

    • Masked arrays are now supported.

  • A mode parameter has been added to KNeighborsGraph; as in scikit-learn, it can be set to either "distance" or "connectivity" (#478).

  • List input is now accepted by all transformers in gtda.graphs, and outputs are consistently either lists or 3D arrays (#478).

  • Sparse matrices returned by KNeighborsGraph and TransitionGraph now have int dtype (0-1 adjacency matrices), and are not necessarily symmetric (#478).


  • Pullback cover set labels and partial cluster labels have been added to Mapper node hovertexts (#445).

  • The functionality of Nerve and make_mapper_pipeline has been greatly extended (#447 and #456):

    • Node and edge metadata are now accessible in output igraph.Graph objects by means of the VertexSeq and EdgeSeq attributes vs and es (respectively). Graph-level dictionaries are no longer used.

    • Available node metadata can be accessed by graph.vs[attr_name] where for attr_name is one of "pullback_set_label", "partial_cluster_label", or "node_elements".

    • Sizes of intersections are automatically stored as edge weights, accessible by["weight"].

    • A "store_intersections" keyword argument has been added to Nerve and make_mapper_pipeline to allow to store the indices defining node intersections as edge attributes, accessible via["edge_elements"].

    • A contract_nodes optional parameter has been added to both Nerve and make_mapper_pipeline; nodes which are subsets of other nodes are thrown away from the graph when this parameter is set to True.

    • A graph_ attribute is stored during

  • Two of the Nerve parameters (min_intersection and the new contract_nodes) are now available in the widgets generated by plot_interactive_mapper_graph, and the layout of these widgets has been improved (#456).

  • ParallelClustering and Nerve have been exposed in the documentation and in gtda.mapper’s __init__ (#447).


  • A plot_params kwarg is available in plotting functions and methods throughout to allow user customisability of output figures. The user must pass a dictionary with keys "layout" and/or "trace" (or "traces" in some cases) (#441).

  • Several plots produced by plot class methods now have default titles (#453).

  • Infinite deaths are now plotted by plot_diagrams (#461).

  • Possible multiplicities of persistence pairs in persistence diagram plots are now indicated in the hovertext (#454).

  • plot_heatmap now accepts boolean array input (#444).

New tutorials and examples

The following new tutorials have been added:

  • Topology of time series, which explains the theory of the Takens time-delay embedding and its use with persistent homology, demonstrates the new API of several components in gtda.time_series, and shows how to construct time series classification pipelines in giotto-tda by partially reproducing arXiv:1910:08245.

  • Topology in time series forecasting, which explains how to set up time series forecasting pipelines in giotto-tda via TransformerResamplerMixin``s and the ``giotto-tda Pipeline class.

  • Topological feature extraction from graphs, which explains what the features extracted from directed or undirected graphs by VietorisRipsPersistence, SparseRipsPersistence and FlagserPersistence are.

  • Classifying handwritten digits, which presents a fully-fledged machine learning pipeline in which cubical persistent homology is applied to the classification of handwritten images from he MNIST dataset, partially reproducing arXiv:1910.08345.


  • A check_collection input validation function has been added (#491).

  • validate_params now accepts "in" and "of" keys simultaneously in the references dictionaries, with "in" used for non-list-like types and "of" otherwise (#502).

Installation improvements

  • pybind11 is now treated as a standard git submodule in the developer installation (#459).

  • pandas is now part of the testing requirements when intalling from source (#508).

Bug Fixes

  • A bug has been fixed which could lead to features with negative lifetime in persistent homology transformers when infinity_values was set too low (#339).

  • By relying on scipy’s shortest_path instead of scikit-learn’s graph_shortest_path, some errors in computing GraphGeodesicDistance (e.g. when som edges are zero) have been fixed (#422).

  • A bug in the handling of COO matrices by the ripser interface has been fixed (#465).

  • A bug which led to the incorrect handling of the homology_dimensions parameter in Filtering has been fixed (#439).

  • An issue with the use of joblib.Parallel, which led to errors when attempting to run HeatKernel, PersistenceImage, and the corresponding amplitudes and distances on large datasets, has been fixed (#428 and #481).

  • A bug leading to plots of persistence diagrams not showing points with negative births or deaths has been fixed, as has a bug with the computation of the range to be shown in the plot (#437).

  • A bug in the handling of persistence pairs with negative death values by Filtering has been fixed (#436).

  • A bug in the handling of homology_dimension_ix (now renamed to homology_dimension_idx) in the plot methods of HeatKernel and PersistenceImage has been fixed (#452).

  • A bug in the labelling of axes in HeatKernel and PersistenceImage plots has ben fixed (#453 and #454).

  • PersistenceLandscape plots now show all homology dimensions, instead of just the first (#454).

  • A bug in the computation of amplitudes and pairwise distances based on persistence images has been fixed (#454).

  • Silhouette now does not create NaNs when a subdiagram is trivial (#454).

  • CubicalPersistence now does not create pairs with negative persistence when infinity_values is set too low (#467).

  • Warnings are no longer thrown by KNeighborsGraph when metric="precomputed" (#506).

  • A bug in Labeller.resample affecting cases in which n_steps_future >= size - 1, has been fixed (#460).

  • A bug in validate_params, affecting the case of tuples of allowed types, has been fixed (#502).

Backwards-Incompatible Changes

  • The minimum required versions from most of the dependencies have been bumped. The updated dependencies are numpy >= 1.19.1, scipy >= 1.5.0, joblib >= 0.16.0, scikit-learn >= 0.23.1, python-igraph >= 0.8.2, plotly >= 4.8.2, and pyflagser >= 0.4.1 (#457).

  • GraphGeodesicDistance now returns either lists or 3D dense ndarrays for compatibility with the homology transformers - By relying on scipy’s shortest_path instead of scikit-learn’s graph_shortest_path, some errors in computing GraphGeodesicDistance (e.g. when som edges are zero) have been fixed (#422).

  • The output of PairwiseDistance has been transposed to match scikit-learn convention (n_samples_transform, n_samples_fit) (#420).

  • plot class methods now return figures instead of showing them (#441).

  • Mapper node and edge attributes are no longer stored as graph-level dictionaries, "node_id" is no longer an available node attribute, and the attributes nodes_ and edges_ previously stored by have been removed in favour of a graph_ attribute (#447).

  • The homology_dimension_ix parameter available in some transformers in gtda.diagrams has been renamed to homology_dimensions_idx (#452).

  • The base of the logarithm used by PersistenceEntropy is now 2 instead of e, and NaN values are replaced with -1 instead of 0 by default (#450 and #474).

  • The outputs of PersistenceImage, HeatKernel and of the pairwise distances and amplitudes based on them is now different due to the improvements described above.

  • Weights are no longer stored in the effective_metric_params_ attribute of PairwiseDistance, Amplitude and Scaler objects when the metric is persistence-image–based; only the weight function is (#454).

  • The homology_dimensions_ attributes of several transformers have been converted from lists to tuples. When possible, homology dimensions stored as parts of attributes are now presented as ints (#454).

  • gaussian_filter (used to make heat– and persistence-image–based representations/pairwise distances/amplitudes) is now called with mode="constant" instead of "reflect" (#454).

  • The default value of order in Amplitude has been changed from 2. to None, giving vector instead of scalar features (#454).

  • The meaning of the default None for weight_function in PersistenceImage (and in Amplitude and PairwiseDistance when metric="persistence_image") has been changed from the identity function to the function returning a vector of ones (#454).

  • Due to the updates in the GUDHI components, some of the bindings and Python interfaces to the GUDHI C++ components in gtda.externals have changed (#468).

  • Labeller.transform now returns a 1D array instead of a column array (#475).

  • PersistenceLandscape now returns 3D arrays instead of 4D ones, for compatibility with the new curves subpackage (#480).

  • By default, CubicalPersistence now removes one infinite bar in H0 (#467, and see above).

  • The former width parameter in SlidingWindow and Labeller has been replaced with a more intuitive size parameter. The relation between the two is: size = width + 1 (#460).

  • clusterer is now a required parameter in ParallelClustering (#508).

  • The max_fraction parameter in FirstSimpleGap and FirstHistogramGap now indicates the floor of max_fraction * n_samples; its default value has been changed from None to 1 (#412).

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Guillaume Tauzin, Julian Burella Pérez, Wojciech Reise, Lewis Tunstall, Nick Sale, and Anibal Medina-Mardones.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.2.2

Major Features and Improvements

  • The documentation for gtda.mapper.utils.decorators.method_to_transform has been improved.

  • A table of contents has been added to the theory glossary.

  • The theory glossary has been restructured by including a section titled “Analysis”. Entries for l^p norms, L^p norms and heat vectorization have been added.

  • The project’s Azure CI for Windows versions has been sped-up by ensuring that the locally installed boost version is detected.

  • Several python bindings to external code from GUDHI, and Hera have been made public: specifically, from gtda.externals import * now gives power users access to:

    • bottleneck_distance,

    • wasserstein_distance,

    • ripser,

    • SparseRipsComplex,

    • CechComplex,

    • CubicalComplex,

    • PeriodicCubicalComplex,

    • SimplexTree,

    • WitnessComplex,

    • StrongWitnessComplex.

    However, these functionalities are still undocumented.

  • The gtda.mapper.visualisation and gtda.mapper.utils._visualisation modules have been thoroughly refactored to improve code clarity, add functionality, change behaviour and fix bugs. Specifically, in figures generated by both plot_static_mapper_graph and plot_interactive_mapper_graph:

    • The colorbar no longer shows values rescaled to the interval [0, 1]. Instead, it always shows the true range of node summary statistics.

    • The values of the node summary statistics are now displayed in the hovertext boxes. A a new keyword argument n_sig_figs controls their rounding (3 is the default).

    • plotly_kwargs has been renamed to plotly_params (see “Backwards-Incompatible Changes” below).

    • The dependency on matplotlib’s rgb2hex and get_cmap functions has been removed. As no other component in giotto-tda required matplotlib, the dependency on this library has been removed completely.

    • A node_scale keyword argument has been added which can be used to controls the size of nodes (see “Backwards-Incompatible Changes” below).

    • The overall look of Mapper graphs has been improved by increasing the opacity of node colors so that edges do not hide them, and by reducing the thickness of marker lines.

    Furthermore, a clone_pipeline keyword argument has been added to plot_interactive_mapper_graph, which when set to False allows the user to mutate the input pipeline via the interactive widget.

  • The docstrings of plot_static_mapper_graph, plot_interactive_mapper_graph and make_mapper_pipeline have been improved.

Bug Fixes

  • A CI bug introduced by an update to the XCode compiler installed on the Azure Mac machines has been fixed.

  • A bug afflicting Mapper colors, which was due to an incorrect rescaling to [0, 1], has been fixed.

Backwards-Incompatible Changes

  • The keyword parameter plotly_kwargs in plot_static_mapper_graph and plot_interactive_mapper_graph has been renamed to plotly_params and has now slightly different specifications. A new logic controls how the information contained in plotly_params is used to update plotly figures.

  • The function get_node_sizeref in gtda.mapper.utils.visualization has been hidden by renaming it to _get_node_sizeref. Its main intended use is subsumed by the new node_scale parameter of plot_static_mapper_graph and plot_interactive_mapper_graph.

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Julian Burella Pérez, Anibal Medina-Mardones, Wojciech Reise and Guillaume Tauzin.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.2.1

Major Features and Improvements

  • The theory glossary has been improved to include the notions of vectorization, kernel and amplitude for persistence diagrams.

  • The ripser function in gtda.externals.python.ripser_interface no longer uses scikit-learn’s pairwise_distances when metric is 'precomputed', thus allowing square arrays with negative entries or infinities to be passed.

  • check_point_clouds in gtda.utils.validation now checks for square array input when the input should be a collection of distance-type matrices. Warnings guide the user to correctly setting the distance_matrices parameter. force_all_finite=False no longer means accepting NaN input (only infinite input is accepted).

  • VietorisRipsPersistence in gtda.homology.simplicial no longer masks out infinite entries in the input to be fed to ripser.

  • The docstrings for check_point_clouds and VietorisRipsPersistence have been improved to reflect these changes and the extra level of generality for ripser.

Bug Fixes

  • The variable used to indicate the location of Boost headers has been renamed from Boost_INCLUDE_DIR to Boost_INCLUDE_DIRS to address developer installation issues in some Linux systems.

Backwards-Incompatible Changes

  • The keyword parameter distance_matrix in check_point_clouds has been renamed to distance_matrices.

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Anibal Medina-Mardones, Julian Burella Pérez, Guillaume Tauzin, and Wojciech Reise.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.2.0

Major Features and Improvements

This is a major release which substantially broadens the scope of giotto-tda and introduces several improvements. The library’s documentation has been greatly improved and is now hosted via GitHub pages. It includes rendered jupyter notebooks from the repository’s examples folder, as well as an improved theory glossary, more detailed installation instructions, improved guidelines for contributing, and an FAQ.

Plotting functions and plotting API

This version introduces built-in plotting capabilities to giotto-tda. These come in the form of:

  • a new plotting subpackage populated with plotting functions for common data structures;

  • a new PlotterMixin and a class-level plotting API based on newly introduced plot, transform_plot and fit_transform_plot methods which are now available in several of giotto-tda’s transformers.

Changes and additions to gtda.homology

The internal structure of this subpackage has been changed. ConsistentRescaling has been moved to a new point_clouds subpackage (see below), and gtda.homology no longer contains a point_clouds submodule. Instead, it contains two submodules, simplicial and cubical. simplicial contains the VietorisRipsPersistence class as well as the following new classes:

  • SparseRipsPersistence,

  • EuclideanCechPersistence.

The cubical submodule contains CubicalPersistence, a new class for computing persistent homology of filtered cubical complexes such as those coming from 2D or 3D greyscale images.

New images subpackage

The new gtda.images subpackage contains classes which, together with gtda.homology.CubicalPersistence, extend the capabilities of giotto-tda to computer vision, by handling input representing binary or greyscale 2D/3D images represented as arrays.

The classes in gtda.images.filtrations are responsible for converting binary image input into greyscale images in a variety of ways. The greyscale output can then be fed to gtda.homology.CubicalPersistence to extract topological signatures in the form of persistence diagrams. These classes are:

  • HeightFiltration,

  • RadialFiltration,

  • DilationFiltration,

  • ErosionFiltration,

  • SignedDistanceFiltration.

The classes in gtda.images.preprocessing perform a variety of preprocessing steps on either binary or greyscale image input, as well as conversion to point cloud format. They are:

  • Binarizer,

  • Inverter,

  • Padder,

  • ImageToPointCloud.

New point_clouds subpackage

ConsistentRescaling is no longer placed in gtda.homology. Instead, it is now in a point_clouds subpackage containing classes which process or modify the geometry of point cloud data. gtda.point_clouds also contains the new class ConsecutiveRescaling, written with time series applications in mind.

List of point cloud input

All classes in the homology subpackage (VietorisRipsPersistence, SparseRipsPersistence, and EuclideanCechPersistence) can now take as inputs to the fit and transform methods lists of 2D arrays instead of simply 3D arrays. In this way, collections of point clouds with varying numbers of points can be processed.

Changes and additions to gtda.diagrams

The diagrams subpackage contains the following new classes:

  • PersistenceImage

  • Silhouette

Additionally, the subpackage has been reorganised as follows:

  • The features submodule now only contains the scalar feature generation classes Amplitude (moved there from distance) and PersistenceEntropy.

  • Classes which produce vector representations from persistence diagrams have been moved to the new representations submodule.

Changes and additions to gtda.utils

  • validate_params has been thoroughly refactored, documented and exposed for the benefit of developers.

  • check_diagrams has been modified, documented and exposed for the benefit of developers.

  • The new check_point_clouds performs validation of inputs consisting of collections of point clouds of distance matrices. It accepts both lists of 2D ndarrays and 3D ndarrays, and is used in the fit and transform methods of classes in gtda.homology.simplicial to allow for list input (see above).

External modules and HPC improvements

A substantial effort has been put in improving the quality of the high-performance components contained in gtda.externals. The end result is a cleaner packaging as well as faster execution of C++ functions due to improved bindings. In particular:

  • Two binaries are now shipped for ripser, one of them being optimised for calculations with mod 2 coefficients.

  • Recent improvements by the authors of the hera C++ library have been integrated in giotto-tda.

  • Compiler optimisations for Windows-based systems have been added.

  • The integration of pybind11 has been improved and several issues arising with CMake and boost during developer installations have been addressed.

Bug Fixes

  • Fixed a bug with TakensEmbedding’s algorithm for search of optimal parameters.

  • Inconsistencies in between the meaning of “bottleneck amplitude” in the theory and in the code have been ironed out. The code has been modified to agree with the theory glossary. The outputs of the gtda.diagrams classes Amplitude, Scaler and Filtering is affected.

  • Fixed bugs affecting color normalization in Mapper graph plots.

Backwards-Incompatible Changes

  • Python 3.5 is no longer supported.

  • Mac OS X versions below 10.14 are no longer supported by the wheels shipped via PyPI.

  • ConsistentRescaling is no longer found in gtda.homology and is now part of gtda.point_clouds.

  • The outputs of the gtda.diagrams classes Amplitude, Scaler and Filtering have changed due to sqrt(2) factors (see Bug Fixes).

  • The meta_transformers module has been removed.

  • The plotting module has been removed from the examples folder of the repository.

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Guillaume Tauzin, Wojciech Reise, Julian Burella Pérez, Roman Yurchak, Lewis Tunstall, Anibal Medina-Mardones, and Adélie Garin.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.1.4

Library name change

The library and GitHub repository have been renamed to giotto-tda! While the new name is meant to better convey the library’s focus on Topology-powered machine learning and Data Analysis, the commitment to seamless integration with scikit-learn will remain just as strong and a defining feature of the project. Concurrently, the main module has been renamed from giotto to gtda in this version. giotto-learn will remain on PyPI as a legacy package (stuck at v0.1.3) until we have ensured that users and developers have fully migrated. The new PyPI package giotto-tda will start at v0.1.4 for project continuity.

Short summary: install via

python -m pip install -U giotto-tda

and import gtda in your scripts or notebooks!

Change of license

The license changes from Apache 2.0 to GNU AGPLv3 from this release on.

Major Features and Improvements

  • Added a mapper submodule implementing the Mapper algorithm of Singh, Mémoli and Carlsson. The main tools are the functions make_mapper_pipeline, plot_static_mapper_graph and plot_interactive_mapper_graph. The first creates an object of class MapperPipeline which can be fit-transformed to data to create a Mapper graph in the form of an igraph.Graph object (see below). The MapperPipeline class itself is a simple subclass of scikit-learn’s Pipeline which is adapted to the precise structure of the Mapper algorithm, so that a MapperPipeline object can be used as part of even larger scikit-learn pipelines, inside a meta-estimator, in a grid search, etc. One also has access to other important features of scikit-learn’s Pipeline, such as memory caching to avoid unnecessary recomputation of early steps when parameters involved in later steps are changed. The clustering step can be parallelised over the pullback cover sets via joblib – though this can actually lower performance in small- and medium-size datasets. A range of pre-defined filter functions are also included, as well as covers in one and several dimensions, agglomerative clustering algorithms based on stopping rules to create flat cuts, and utilities for making transformers out of callables or out of other classes which have no transform method. plot_static_mapper_graph allows the user to visualise (in 2D or 3D) the Mapper graph arising from fit-transforming a MapperPipeline to data, and offers a range of colouring options to correlate the graph’s structure with exogenous or endogenous information. It relies on plotly for plotting and displaying metadata. plot_interactive_mapper_graph adds interactivity to this, via ipywidgets: specifically, the user can fine-tune some parameters involved in the definition of the Mapper pipeline, and observe in real time how the structure of the graph changes as a result. In this release, all hyperparameters involved in the covering and clustering steps are supported. The ability to fine-tune other hyperparameters will be considered for future versions.

  • Added support for Python 3.8.

Bug Fixes

  • Fixed consistently incorrect documentation for the fit_transform methods. This has been achieved by introducing a class decorator adapt_fit_transform_docs which is defined in the newly introduced

Backwards-Incompatible Changes

  • The library name change and the change in the name of the main module giotto are important major changes.

  • There are now additional dependencies in the python-igraph, matplotlib, plotly, and ipywidgets libraries.

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Lewis Tunstall, Guillaume Tauzin, Philipp Weiler, Julian Burella Pérez.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.1.3

Major Features and Improvements


Bug Fixes

  • Fixed a bug in diagrams.Amplitude causing the transformed array to be wrongly filled and added adequate test.

Backwards-Incompatible Changes


Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Major Features and Improvements

  • Added support for Python 3.5.

Bug Fixes


Backwards-Incompatible Changes


Thanks to our Contributors

This release contains contributions from many people:

Matteo Caorsi, Henry Tom (@henrytomsf), Guillaume Tauzin.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.1.1

Major Features and Improvements

  • Improved documentation.

  • Improved features of class Labeller.

  • Improved features of class PearsonDissimilarities.

  • Improved GitHub files.

  • Improved CI.

Bug Fixes

Fixed minor bugs from the first release.

Backwards-Incompatible Changes

The following class were renamed: - class PearsonCorrelation was renamed to class PearsonDissimilarities

Thanks to our Contributors

This release contains contributions from many people:

Umberto Lupo, Guillaume Tauzin, Matteo Caorsi, Olivier Morel.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.

Release 0.1.0

Major Features and Improvements

The following submodules where added:

  • giotto.homology implements transformers to modify metric spaces or generate persistence diagrams.

  • giotto.diagrams implements transformers to preprocess persistence diagrams or extract features from them.

  • giotto.time_series implements transformers to preprocess time series or embed them in a higher dimensional space for persistent homology.

  • giotto.graphs implements transformers to create graphs or extract metric spaces from graphs.

  • giotto.meta_transformers implements convenience giotto.Pipeline transformers for direct topological feature generation.

  • giotto.utils implements hyperparameters and input validation functions.

  • giotto.base implements a TransformerResamplerMixin for transformers that have a resample method.

  • giotto.pipeline extends scikit-learn’s module by defining Pipelines that include TransformerResamplers.

Bug Fixes


Backwards-Incompatible Changes


Thanks to our Contributors

This release contains contributions from many people:

Guillaume Tauzin, Umberto Lupo, Philippe Nguyen, Matteo Caorsi, Julian Burella Pérez, Alessio Ghiraldello.

We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions. In particular, we would like to thank Martino Milani, who worked on an early prototype of a Mapper implementation; although very different from the current one, it adopted an early form of caching to avoid recomputation in refitting, which was an inspiration for this implementation.

Release 0.1a.0

Initial release of the library, originally named giotto-learn.